The proposed research concerns the inference of historical patterns of migration. Traditional population genetic models of migration assume that populations have been exchanging migrants at a constant rate over long periods of time. For many species, however, this assumption may not be appropriate. Therefore, the development of a computational method to test for recent changes in migration rate and to estimate the relevant demographic parameters is proposed. While most methods of demographic inference assume that all of the genetic markers being studied are independent (unlinked), this approach will take advantage of the patterns of linkage along a recombining chromosome. By considering this linkage information (specifically, the lengths of DNA segments that inferred to have migrant origin), one can go beyond estimating how much migration has occurred between two populations, and say something about when, historically, this migration occurred. During the first phase of this project, the effect of various population histories on the length distribution of migrant DNA segments will be investigated, making use of an existing simulation program (ms) and inference method (structure 2.0). Next, the new inference method described above will be developed, using Markov chain Monte Carlo methodology in a maximum likelihood or Bayesian framework. Finally, this method will be applied to existing human polymorphism data sets (both SNP and microsatellite) in order to test the null hypothesis that migration among human populations has been constant since their divergence. This analysis will permit the estimation of demographic parameters for admixed human populations, and will therefore aid in the selection of populations for admixture mapping studies of disease association. Relevance to public health: The goal of the proposed research is to test for historical changes in the rate of migration between populations, and to estimate quantities such as the time since a migration rate change and the magnitude of such a change. The computational method developed will have a variety of applications, including the estimation of demographic parameters in human populations with a history of recent admixture (ancestry from multiple sources), such as African-American, Hispanic, Central Asian and Northern African populations. That information will be relevant in assessing the utility of such populations for admixture mapping studies, which aim to identify genetic variants associated with complex diseases that occur at different frequencies in different populations.